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67 results for “cyclists”
Bicycle trips collected using Cyclists Geo-C geo-game
<p>This is an experimental dataset for the bicycle trips recorded using and geo-game called "Cyclist Geo-C". It contains the geometry of the trips recorded by 60 participants from three European Cities: Münster, Germany; Castelló, Spain; Valletta, Malta. This dataset was collected and analysed for the PhD Thesis "Mobile Services for Green Living" part of the European Joint Doctorate in Geoinformatics and the <a href="http://geo-c.eu/">Geo-C </a>Project. </p> <p>The dataset is composed of three subsets.</p> <ol> <li>There is a point dataset called "<em><strong>trips_od.geojson</strong></em>" which contained the point geometries where each trip started and ended with attributes for latitude, longitude, altitude, and precision coordinates. Each point also had the timestamp which indicates the time when the user started or ended the trip.</li> <li>There is a line dataset called "<em><strong>segments.geojson</strong></em>" which contained the geometries of the straight lines connecting two locations of the participant. Each segment started from an initial point "p<sub>i</sub>" recorded at a "t<sub>i</sub>” and ended at the next point recorded by the user "p<sub>f</sub>” at time “t<sub>f</sub>”. The time difference between "t<sub>i</sub>” and “t<sub>f</sub>” was at most five minutes while the length of the segment was at most one kilometre. Each segment also had the participant and trip identifier, and the segment's sequence number within the trip For each of the trip segments, we calculated the distance and speed using the recorded coordinates and timestamps from "p<sub>i</sub>" and "p<sub>f</sub>" points. <span class="math-tex">\(trip\_segment = f(p_i,p_f)\)</span> and <span class="math-tex">\(segment\_speed = \frac{distance(p_i,p_f)}{\Delta time(p_i,p_f)}\)</span>. Then we classified the segments according to the calculated distance as: “<em>walking segment</em>” when the calculated speed was less than 5 km/h; “<em>cycling segment</em>” when the calculated speed was between 5 and 50 km/h; or “<em>non-cycling segment</em>” when the calculated speed was more than 50 Km/h.</li> <li>There was another line dataset called “<em><strong>trips_tags.geojson </strong></em>” which contained the geometries of each of the trip paths. A trip was a line (also called polyline by GIS users) defined by the ordered sequence of trip segments. It started from origin point "p<sub>i</sub>" of the trip’s first segment and ended at the destination point "p<sub>f</sub>" of the trip's last segment. Each trip also had the participant's identification, trip's identification, the number of segments, start and end times.</li> </ol> <p>In addition to the experimental dataset recorded by participants, our analysis used a secondary dataset to define a comparable framework for the three cities. The secondary dataset consisted of the existing bicycle paths in the cities of Münster and Castelló as well as the planned bicycle paths around Valletta. For the city of Münster, the source of the bicycle paths was the <a href="http://www.openstreetmap.org">OpenStreetMap</a> (we downloaded the line elements with the tags “<em>bicycle=yes</em>” and "<em>cycleway=yes</em>”). For the city of Castelló, we obtained the bicycle paths from the city transport authority, including the city of Valletta, we created a digital version of the national bicycle network plan.</p> <p>We estimated the number of trips "<em><strong>bikepaths_trips.geojson</strong></em>" and the number of segments "<em><strong>bikepaths_segments</strong></em><em><strong>.</strong></em><em><strong>geojson</strong></em>" at each bike path. Also, we provide the areas where participants faced frictions during the experiment which corresponded to low cycling speeds "frictions.geojson".</p> <p>Finally, we provide a visual reference of the dataset in "<em><strong>frictions_cities.pdf</strong></em>".</p>
SIMUSAFE cyclist behavior in simulator and in real-world
<p>This dataset includes data collected by the SIMUSAFE H2020 EU project (2017-2021) during its first data acquisition cycle. Voluntary bicycle riders are the subjects in this dataset, and the dataset includes a combination of sensory and psychological characteristics data. Sensory data was recorded in one of two settings: driving around the city in reality (NDT) and driving in a simulator (NST) along routes that were designed to imitate similar in-city driving.</p> <p>Overall, the dataset consists of recordings from 7 subjects for the following durations:</p> <p><strong>Total time per user (hours):</strong></p> <p>User Overall recording duration</p> <p>USER0 0 days 17:28:06.284997888</p> <p>USER1 0 days 04:25:50.084999680</p> <p>USER2 0 days 00:11:50.677999616</p> <p>USER3 0 days 01:30:38.754000640</p> <p>USER4 0 days 02:44:50.625000192</p> <p>USER5 0 days 00:24:49.070000128</p> <p>USER6 0 days 01:15:46.702999040</p> <p><strong>Data measurements and computed attributes:</strong></p> <p><strong><em>GPS </em></strong>coordinates, acquired by a real GPS receiver in NDT and via a simulated receiver in NST. We extracted <em>velocity </em>from the GPS measurements, computed as the distance between every two subsequent coordinates divided by their corresponding timestamps. As a second derivative, <em>Acceleration</em> was then also derived from the difference of the above-mentioned velocity change between the two subsequent points divided by the time-delta.</p> <p><strong><em>Accelerometer </em></strong>data were used to compute the Euclidean norm of the acceleration (a.k.a l<sup>2</sup>-norm) over the acceleration coordinates vector (i.e., {a<sub>x</sub>; a<sub>y</sub>; a<sub>z</sub>}) at each point in time. This feature is sometimes also referred to as the <em>energy-expenditure</em> of the motion.</p> <p><strong>Additional features:</strong></p> <p><strong><em>De/Acceleration {high / low / none}</em></strong>, computed per user per scenario. For each user, acceleration measurements were partitioned by quartiles and were computed per scenario. <em>High-Acceleration </em>was defined as values above the 3<sup>rd</sup> quartile and <em>low-acceleration</em> as values below. <em>No-acceleration</em> was denoted for the case of acceleration is equal to zero. Respectively, decelerations were computed in an equivalent manner, computed from the partitioning of negative acceleration values.</p> <p><strong>Data preparation & preprocessing</strong></p> <ul> <li>GPS coordinates were de-duplicated w.r.t subsequent entries.</li> <li>To avoid issues originating from weak/loss of GPS signal, entries were partitioned into sessions. A session is defined as a sequence of entries with time-deltas no larger than 10 seconds. Velocity & acceleration were derived based on time-deltas within sessions.</li> <li>Rows with a velocity above or equal to 50km/h were filtered out based on the assumption that a regular bike rider won't reach such speeds.</li> </ul> <p>In addition to the data sources and processing procedures mentioned above, the data has been processed according to the following. Per each subject & scenario, the data was partitioned into windows of 30 seconds using a sliding window with overlap. On each window 5 statistics were computed I.e., entropy, mean, variance, skew, kurtosis on 3 different sources: GPS-based velocity, GPS-based acceleration, and accelerometer-based magnitude. Achieving a total of 15 features.</p> <p><strong>Data Schema</strong></p> <p>The data comprises measurement data, data computed after windowing as well as subject psychometric evaluation data. Window data is computed with a sliding window of size 40 (samples) with an overlap of 20 samples. Before computing windows, the measurement data is filtered from entries with missing ‘v_gps’ (velocity computed from GPS coordinates) values.</p> <p>The measurement dataset is in the attached bicycle_cycle_1_measurement_data.csv file.</p> <p>Dataset computed with windowing is in the attached bicycle_cycle_1_windowed_data_w_computed_features.csv file.</p> <p>The psychometric evaluation dataset is in the attached bicycle_cycle_1_subject_psychometric_evaluation.csv file.</p> <p>Description of all dataset attributes in all three datasets is detailed in the Data description.docx file.</p> <p>Preliminary correlations identified in the dataset is detailed in BICYCLE-DATA-CORRELATIONS.pptx file</p>
Cyclist Actions: Optical Flow Sequences and Trajectories
<p>The dataset consists of over 1.1 million samples of labeled cyclists actions. Every sample consists of two optical flow sequences, recorded over the past second (9 optical flow images each), from two different cameras, the past trajectory of the cyclist of the last second (50 past positions), and a label of the currently performed action.</p> <p>The samples were extracted from 1,639 video sequences of cyclists moving across an urban intersection at the University of Applied sciences in Aschaffenburg: <a href="https://www.th-ab.de/ueber-uns/organisation/labor/kooperative-automatisierte-verkehrssysteme/ausstattung/">https://www.th-ab.de/ueber-uns/organisation/labor/kooperative-automatisierte-verkehrssysteme/ausstattung/</a></p> <p>The uploaded files consist of an archive containing 27 numpy files, a single numpy file containing trajectories only, and a json file containing 5-fold cross validation/test split.</p> <p>The numpy files consist of python dictionaries with scenes of the form:</p> <pre><code class="language-python">{SCENE_NAME: 'of_hk1/2': [...], # zip compressed, python pickled optical flow sequences of cameras 1/2 'x/y/z_tracked': [...], # tracked cyclists positions in x/y/z directions, 'x/y/z_smoothed': [...], # smoothed (by rts smoother) cyclists positions in x/y/z directions, 'orientation': [...], # orientation of the cyclists estimated by kalman filters 'ts': [...], # utc timestamps in micro seconds LABEL_NAME: [...], # labels of different actions (0 or 1)}</code></pre> <p>The manually created labels are:</p> <ul> <li>straight: cyclists is moving and not turning</li> <li>tr/tl: cyclist is turning left/right</li> <li>move: cyclist is moving with nearly constant velocity and not turning</li> <li>start: cyclist was standing and starts moving</li> <li>starting_movement: first movement of cyclist before starting</li> <li>stop: cyclist was moving/starting and slows down to a halt</li> <li>wait: cyclist is standing</li> <li>hand_signal_left/right: cyclist indicates a turn by hand signal</li> <li>shoulder_check_left/right: cyclist looks over left/right shoulder</li> <li>out_of_saddle: cyclist is standing</li> </ul> <p>The optical flow sequences were created using PWC-Net [1].</p> <p>To extract the zipped/pickled optical flow sequences:</p> <pre><code class="language-python">import cv2 as cv import zlib import pickle import numpy as np # visualize flow def vis_of(of): hsv = np.zeros([of.shape[0], of.shape[1], 3], dtype=np.uint8) hsv[..., 1] = 255 mag, ang = cv.cartToPolar(of[..., 0].astype(np.float32), of[..., 1].astype(np.float32)) hsv[..., 0] = ang * 180 / np.pi / 2 hsv[..., 2] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX) bgr = cv.cvtColor(hsv, cv.COLOR_HSV2BGR) return bgr # load npy file from dataset npy_path = 'of_dataset_0.npy' data = np.load(npy_path, allow_pickle=True).item() scene = data[list(data.keys())[0]] # extract optical flow sequence ofs = pickle.loads(zlib.decompress(scene['of_hk1'][i])).astype(np.float16) * 2.0 / 255.0 - 1.0 # show of images in sequence for j in range(len(ofs)): # create bgr image from 2 channel optical flow bgr = vis_of(ofs[j]) cv.imshow("of", bgr) </code></pre> <p>Python code and a description to read the dataset can be found in our GitHub: <a href="https://github.com/CooperativeAutomatedTrafficSystemsLab/CyclistActionRecognition">https://github.com/CooperativeAutomatedTrafficSystemsLab/CyclistActionRecognition</a></p> <p>[1] D. Sun, X. Yang, M. Liu, and J. Kautz, “PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, June 2018, pp. 8934–8943.</p> <p> </p> <p>This work results from the project DeCoInt 2, supported by the German Research Foundation (DFG) within the priority program SPP 1835: "Kooperativ interagierende Automobile", grant numbers DO 1186/1-2, FU 1005/1-2, and SI 674/11-2. Additionally, the work is supported by "Zentrum Digitalisierung Bayern".</p> <p>Due to privacy laws in germany, we are not permitted to publish image sequences.</p>
Dataset of Motion Capture of Cyclists
<p>The system used is the Phasespace Impulse X2E motion capture system, featuring active LEDs. This system uses 24 cameras designed for capturing 3D motion through modulated LEDs. These cameras incorporate pairs of linear scanner arrays operating at high frequencies, enabling the capture of the position of bright spots of light generated by the LEDs. </p> <p><span>Methodology for Mocap Data Collection</span><br><span>Participants received detailed information about the mocap data </span><span>collection procedure and purpose. Informed consent was obtained, </span><span>ensuring understanding and agreement and a pre-session ques</span><span>tionnaire collected demographic and health information. Next, the </span><span>participant’s road or time trial (TT) bike was placed on a turbo</span><br><span>trainer and the meticulous marker placement on the cyclist’s body.</span></p> <p><span>Cyclists followed a specific workout plan based on their bike </span><span>type. The plan included warm-up, cycling positions, and recovery </span><span>intervals.</span></p> <p><br><span>Sensors were strategically put mostly on the back side of the </span><span>torso due to the body position while cycling.</span></p> <p><br><span>Workout:</span></p> <p><span>- Warm-Up: 1 minute in any position.</span><br><span>- 30 seconds at 60 RPM</span><br><span>- 15 seconds at 75 RPM</span><br><span>- 15 seconds at 90 RPM</span><br><span>- 10 seconds at 100 RPM</span><br><span>- 10 seconds at 110 RPM</span></p> <p><br><span>Cycling Positions and Cadences</span></p> <p><span>For Road Bikes:</span></p> <p><span>The workout was performed for each of the following positions </span><span>with 1 minute between each position to allow for recuperation:</span><br><span>- Straight Arms</span><br><span>- Comfortable</span><br><span>- Aggressive</span><br><span>- Aero Position</span><br><span>- Standing (not above 90 RPM)</span></p> <p><span>For TT Bikes:</span></p> <p><span>The workout was performed for each of the following positions </span><span>with 1 minute between each position to allow for recuperation:</span><br><span>- Comfortable</span><br><span>- Aero Position</span><br><span>- Standing (not above 90 RPM)</span></p> <p><span>Participants were monitored throughout the session to ensure </span><span>well-being and comfort. They had the option to terminate the </span><span>procedure if they feel unwell or wish to stop.</span></p> <p><span>The motion capture dataset is organized as follows.</span><br><span>There is a dedicated folder for each participant, labeled accord</span><span>ing to the following naming convention:</span></p> <p><span>"1_RB_M42_20230719_PK"</span></p> <ul> <li><span>1 = Index Number</span></li> <li>RB = Road Bike (RB) or Time Trial bike (TT) </li> <li>M42 = Gender (M/F) and age</li> <li><span>20230719 = Capture Date (YYYYMMDD)</span></li> <li><span>PK = Unique Identifier of participant</span></li> </ul> <p><span>In each folder there are three files:</span></p> <ol> <li><span>C3D = Motion Capture Raw Data</span></li> <li><span>BVH = BioVision Hierarchy (<em>BVH</em>), data mapped to skeletal data ready for animation (errors may still remain)</span></li> <li><span>GPX = Data from related zwift workout</span></li> </ol> <p><span>The Project (Smart Cyclo) is funded by the European Union Recovery and Resilience Facility of the NextGenerationEU instrument, through the Research and Innovation Foundation.</span></p>
Urban Transportation Infrastructure and Cyclist and Pedestrian Safety
<p>The goal of this project was to perform a comprehensive evaluation of crash causes and risk factors to identify the root causes of crashes involving bicyclists and pedestrians in San Antonio, TX. The research included the development of a database of bicycle and pedestrian crash reports in the target area, calculation of crash counts and rates, identifying road segments and intersections with highly concentrated bicycle and pedestrian crashes, and the development of effective safety countermeasures. Several variables and factors were analyzed, including driver characteristics such as age and gender, road-related factors, and environmental factors such as weather conditions and time of the day. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian/bicyclist crashes. Geospatial analysis was used to investigate crash frequency and severity. High-risk locations were identified through heat maps and hotspot analysis. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. The strongest predictors of severe injury include lighting condition, road class, road speed limit, traffic control, collision type, and the age and gender of the pedestrian/bicyclist. Fatal and incapacitating injury risk increased substantially when the pedestrian/bicyclist was at fault. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, and raised medians, and the use of leading pedestrian/bicyclist interval and hybrid beacons are recommended.</p>
Shorter CWR cycling tests as proxies for longer tests in highly trained cyclists
<p class="MsoNormal">Severe-intensity constant work rate (<span>CWR) cycling tests </span>simulate the high-intensity competition environment<span> and are </span>useful for monitoring training progression and adaptation, yet <span>impose significant </span>physiological and psychological strain, require substantial recovery, and may disrupt athlete <span>training or competition preparation.</span> A brief, minimally fatiguing test providing comparable information is desirable.<strong><em> </em></strong></p> <p class="MsoNormal"><strong>Purpose</strong>: To determine whether physiological variables measured during, and functional decline in maximal power output immediately after, a <span>2-min</span> CWR test can act as a proxy for <span>4-min</span> test outcomes.</p> <p class="MsoNormal"><strong>Methods</strong>: <span>Physiological stress (</span><span>V̇O<sub>2</sub> kinetics, heart rate, blood lactate concentrations </span>([La<sup>-</sup>]<sub>b</sub>)<span>) </span><span>was monitored and performance fatigability was estimated (</span>as p<span>re-to-post-CWR changes in 10-s sprint power) during 2- and 4-min CWR tests in 16 high-level cyclists (</span>V̇O<sub>2peak</sub>=64.4±6.0 ml∙kg<sup>-1</sup>∙min<sup>-1</sup>). <span>The relationship between the 2- and 4-min CWR tests and the physiological variables that best relate to the performance fatigability were investigated.</span></p> <p class="MsoNormal"><strong>Results</strong>: The 2-min CWR test evoked a smaller decline in sprint mechanical power (32% vs. 47%, <em>p</em><0.001). Both the physiological variables (r=0.66-0.96) and sprint mechanical power (r=0.67-0.92) were independently and strongly correlated between 2- and 4-min tests. Differences in <span>V̇O<sub>2peak</sub></span> and [La<sup>-</sup>]<sub>b</sub> in both CWR tests were strongly associated with the decline in sprint mechanical power.</p> <p class="MsoNormal"><strong>Conclusion</strong>: Strong correlations between 2- and 4-min severe-intensity CWR test outcomes indicated that the shorter test can be used as a proxy for the longer test. A shorter test may be more practical within the elite performance environment due to lower physiological stress and performance fatigability and should have less impact on subsequent training and competition preparation.</p>
Cyclist Actions: Motion History Images and Trajectories
<p>The dataset consists of over 1.1 million samples of labeled cyclists actions. Every sample consists of two motion history images (MHI), consisting of motion over the past second, from two different cameras, the past trajectory of the cyclist of the last second (50 past positions), and a label of the currently performed action.</p> <p>The samples were extracted from 1,639 video sequences of cyclists moving across an urban intersection at the University of Applied sciences in Aschaffenburg: <a href="https://www.th-ab.de/ueber-uns/organisation/labor/kooperative-automatisierte-verkehrssysteme/ausstattung/">https://www.th-ab.de/ueber-uns/organisation/labor/kooperative-automatisierte-verkehrssysteme/ausstattung/</a></p> <p>The uploaded files consist of a numpy file with MHI, trajectories and labels, and a json file containing 5-fold cross validation/test split.</p> <p>Every MHI has a size 192x192x2. The two channels contain the separate motion contours of the bike and the cyclist.</p> <p>The manually created labels are:</p> <ul> <li>straight: cyclists is moving and not turning</li> <li>tr/tl: cyclist is turning left/right</li> <li>move: cyclist is moving with nearly constant velocity and not turning</li> <li>start: cyclist was standing and starts moving</li> <li>starting_movement: first movement of cyclist before starting</li> <li>stop: cyclist was moving/starting and slows down to a halt</li> <li>wait: cyclist is standing</li> <li>hand_signal_left/right: cyclist indicates a turn by hand signal</li> <li>shoulder_check_left/right: cyclist looks over left/right shoulder</li> <li>out_of_saddle: cyclist is standing</li> </ul> <p>The numpy file consists of python dictionaries with scenes of the form:</p> <pre><code class="language-python">{SCENE_NAME: 'mhi_hk1/2': [...], # list of zip compressed, python pickled mhi from cameras 1 and 2 'x/y/z_tracked': [...], # tracked cyclists positions in x/y/z directions, 'x/y/z_smoothed': [...], # smoothed (by rts smoother) cyclists positions in x/y/z directions, 'orientation': [...], # orientation of the cyclists estimated by kalman filters 'ts': [...], # utc timestamps in micro seconds LABEL_NAME: [...], # labels of different actions (0 or 1)}</code></pre> <p>The zipped/pickled mhi can be decompressed using:</p> <pre><code class="language-python">import numpy as np import pickle import zlib d = np.load('mhi_dataset_0,1,2,3,4,6,9,13,18,26,33,41,49.npy', allow_pickle=True, encoding='latin1').item() for scene_name in d: for i in range(len(d[scene_name]['mhi_hk1']) - 50): # ommit 50 first frames as they have no mhi mhi1 = pickle.loads(zlib.decompress(d[scene_name]['mhi_hk1'][49 + i].encode('latin1')), encoding='latin1') mhi2 = pickle.loads(zlib.decompress(d[scene_name]['mhi_hk2'][49 + i].encode('latin1')), encoding='latin1')</code></pre> <p> </p> <p>This work results from the project DeCoInt 2, supported by the German Research Foundation (DFG) within the priority program SPP 1835: "Kooperativ interagierende Automobile", grant numbers DO 1186/1-2, FU 1005/1-2, and SI 674/11-2. Additionally, the work is supported by "Zentrum Digitalisierung Bayern".</p> <p>Due to privacy laws in germany, we are not permitted to publish raw image data.</p>
Cyclists survey, Salzburg, May 2019
<p>Results of an online survey in May 2019 with 125 participants in Salzburg on what would motivate them to cycle more often. Results have been reported in Stabauer P., Hornung-Prähauser V. & Schrempf B. (2019): Behavioral intervention design for scaling innovative services promoting smart sustainability. Paper presented at the 2019 ISPIM Innovation Conference, Florence, Italy, 16-19 June 2019.</p>
Cyclists survey, Uppsala, September 2019
<p>Results of an online survey in September 2019 with 216 participants in Uppsala, Sweden, on what would motivate them to cycle more often.</p>
An orally angiotensin - (1 – 7) inclusion compound reduce time to reaction in 2 stroop task and modify heart rate variability after continuous test in mountain 3 bike cyclists
<p>data for An orally angiotensin - (1 – 7) inclusion compound reduce time to reaction in 2 stroop task and modify heart rate variability after continuous test in mountain 3 bike cyclists,<br> </p> <p>Recently our group showed that hydroxypropyl β-cyclodextrin (HPβ-CD)-Angiotensin-(1-7) (HPβ-CD-Ang-[1-7]) oral formulation affects performance and decreases the perceived effort of mountain bike (MTB) athletes.</p> <p>Twenty-one male MTB practitioners were divided into a continuous protocol time trial and repeated sprint groups. Three hours before a 20-km cycling time trial or 4×30-s repeated all-out sprints on a leg cycle ergometer, the athletes received HPβ-CD-Ang-(1-7) (0.8 mg) or HPβ-CD-placebo (only HPβ-CD) oral capsules over a 7-day interval randomized crossover design. At rest and immediately after the exercise protocol, the ratings of perceived recovery and the visual analog scale were assessed, and the volunteers completed the Stroop task (ST). Heart rate variability was measured at rest and peak effort. There were no differences in the perceived variables. The ST showed that HPβ-CD-Ang-(1-7) supplementation reduced the reaction time (rest 1032±331 ms vs. after protocol 902±286 ms, p=0.05) after the continuous time trial. The withdrawal of the parasympathetic components in the peak effort to the continuous protocol was not different from that of rest in the HPβ-CD-Ang-(1-7) condition. The results are pioneering, especially in humans, but indicate that Angiotensin-(1-7) potentially affects reaction time and the parasympathetic withdrawal after continuous protocol time trial.</p>
Effect of Infusion of Yerba Mate on Oxidative Stress, Physiology, and Performance of Trained Cyclists
ClinicalTrials.gov study NCT04642144. IPD Sharing: NO. Countries: 1. Publications: 1.
Shorter CWR cycling tests as proxies for longer tests in highly trained cyclists
Open the record for dataset details and reuse information.
Excel Spreadsheet of data for CPBS Report 23UNM04 - Understanding External Factors and Workload's Impact on Cyclist Safety
<p>Excel spreadsheet data.</p>
Data for "Computational Desire Line Analysis of Cyclists on the Dybbølsbro Intersection in Copenhagen"
<p>Data for the paper "Computational Desire Line Analysis of Cyclists on the Dybbølsbro Intersection in Copenhagen"</p> <p>**Paper (preprint)**: [https://arxiv.org/abs/2211.01301](https://arxiv.org/abs/2211.01301) </p>
COUREUR Study Myocardial Inflammation in Cyclist Part 2
ClinicalTrials.gov study NCT05138003. IPD Sharing: Not stated. Countries: 2. Publications: 1.
The Effect of Diet Composition on Performance, Expenditure, Blood Lipids, and Appetite Hormones in Highly Trained Cyclists
ClinicalTrials.gov study NCT04097171. IPD Sharing: YES. Countries: 1. Publications: 1.
Effects of a CBD and Natural Ingredients Cream on the Chronic Pain of Triathletes, Runners, Cyclists, and Swimmers
ClinicalTrials.gov study NCT06771076. IPD Sharing: NO. Countries: 1. Publications: 5.
Probiotic Supplementation and Endurance Performance in Cyclists and Masters Runners
ClinicalTrials.gov study NCT02756221. IPD Sharing: YES. Countries: 1. Publications: 3.
Overuse Injuries in Road Cyclists, Prevalence and Prevention
ClinicalTrials.gov study NCT02106715. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Acute Nitrate Supplementation in Cyclists
ClinicalTrials.gov study NCT01384968. IPD Sharing: Not stated. Countries: 1. Publications: 21.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.